Generative Artificial Intelligence for Biology: Toward Unifying Models, Algorithms, and Modalities

Rapid advances in generative artificial intelligence have revolutionized biological modeling across domains such as protein, genetics, and single-cell. However, existing works often organize applications by molecule types or specific research tasks, overlooking the methodological convergence and cross-modal innovations. This paper aims to present a unified methodological perspective that highlights the fundamental technical commonalities across biological modalities. We systematically organize recent advances in generative modeling for biology through the lens of core machine learning paradigms, from language models (LMs) and diffusion models to their emerging hybrid architectures. Our work reveals how techniques initially developed for one molecular type (e.g., protein design) can be effectively transferred to others (e.g., RNA engineering), and identifies the convergence trend where discrete diffusion models and iterative language models represent different facets of a unified generative framework. We cover the evolution from domain-specific models to multi-modal biological foundation models and agent-based systems. By emphasizing methodological connections rather than applications, this paper aims to accelerate cross-domain innovation and make the field more accessible to the broader machine learning community. We conclude by identifying promising research directions where successful techniques in one biological domain remain unexplored in others, offering a roadmap for future advances in generative biology.